To date, LiDAR sensors are massively used to source ground-truth data in primary scientific datasets, such as KITTI [1]}, DrivingStereo [2]}, and many others [3]}, [4]}, [5]}, powering state-of-the-art deep learning techniques in computer vision. However, when projecting the depth map over the camera image plane, the issue mentioned above is usually tackled by enforcing consistency between the depth map and the values obtained through a stereo algorithm [6]}, [7]} or deep stereo network [2]}. Nonetheless, these approaches have some flaws as well: i) they require stereo cameras during acquisition with the LiDAR and ii) they do not filter out only the LiDAR errors, but also the stereo algorithm errors, thus affecting the cleaned data with the intrinsic limitations of the stereo setup (e.g., filtering out depth measures in textureless areas). Despite these limitations, these approaches are viable when pre-processing a dataset beforehand is feasible. However, they might not be applicable in real applications where a stereo setup is unavailable or when the depth labels for training are needed at runtime, for instance, to adapt stereo networks online [9]}.
